«Encoding loci». Problems and prospects using of FACS in the interpretation of emotions and states in Maya terracotta (1st millennium AD)

 
PIIS0044748X0021217-7-1
DOI10.31857/S0044748X0021217-7
Publication type Article
Status Published
Authors
Occupation: expert of the scientific center
Affiliation: Nizhny Novgorod Institute of Management – branch of RANEPA
Address: Russian Federation, Nizhny Novgorod
Journal nameLatin America
EditionIssue 8
Pages82-100
Abstract

The article is devoted to the study of emotions and psychoemotional states represented on Mayan terracotta (1st millennium AD.) The key direction of the analysis was the adaptation of the Facial Action Coding System (FACS) that had not been previously used when working with archaeological material. The study of a representative sample of figurines in the amount of more than 300 units made it possible to determine the Action Units (AU) represented on the faces of the characters in all three floors of the face. The combination of AU on terracotta gave an opportunity to determine the features of almost all basic groups of emotions. Much attention is paid to the characteristics of Action Descriptors (AD) and the system for detecting various wrinkles, which allowed to reflect to some extent the emotional states of the depicted characters.

The article discusses in detail the difficulties of FACS adaptation related to the degree of preservation of the material, iconographic features of images, anthropological specifics, static, which do not allow fixing some Action Units, due to their dynamism (blinking, swallowing, etc.). Despite these difficulties, the article describes successful application of FACS and the prospects for work in this area, expressed in the creation of software by definition AU and AD for Maya terracotta. In the future, this will simplify the mechanism for determining emotions and allow extrapolating images to their "historical prototypes".

KeywordsMaya, terracotta, figurative plastic, emotions, Facial Action Coding System, Action Unit, Action Descriptor
Received11.02.2022
Publication date24.08.2022
Number of characters37678
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